Construction and External Validation of a Ferroptosis-Related Gene Signature of Predictive Value for the Overall Survival in Bladder Cancer

Purpose: To identify whether ferroptosis-related genes play predictive roles in bladder cancer patients and to develop a ferroptosis-related gene signature to predict overall survival outcomes.Materials and Methods: We downloaded the mRNA expression files and clinical data of 256 bladder samples (188 bladder tumour and 68 nontumour samples) from the GEO database and 430 bladder samples (411 bladder tumour and 19 nontumour samples) from the TCGA database. A multigene signature based on prognostic ferroptosis-related genes was constructed by least absolute shrinkage and selection operator Cox regression analysis in the GEO cohort. The TCGA cohort was used to validate the ferroptosis-related gene signature. Next, functional enrichment analysis, including both Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses, was performed to elucidate the mechanism underlying the signature. The ssGSEA scores of 16 immune cells and 13 immune-related pathway activities between the high-risk and low-risk groups were also analysed in our study.Results: Thirty-three (67.3%) ferroptosis-related genes were differentially expressed between bladder tumour samples and nontumour samples in the GEO cohort. The intersection of prognostic ferroptosis-related genes and differentially expressed genes identified four prognostic targets, including ALOX5, FANCD2, HMGCR and FADS2. The least absolute shrinkage and selection operator Cox regression successfully built a 4-gene signature: risk score value = esum (each gene’s normalized expression * each gene’s coefficient). Univariate and multivariate Cox regression analyses were performed in both the GEO and TCGA cohorts to test the independent prognostic value of the 4-gene risk signature. Multivariate Cox regression analysis in the GEO cohort identified age (p < 0.001), grade (p = 0.129) and risk score (p = 0.016) as independent prognostic predictors for overall survival. Multivariate Cox regression analysis in the TCGA cohort also identified age (p = 0.002), stage (p < 0.001) and risk score (p = 0.006) as independent prognostic predictors for overall survival. The type II IFN response was determined to be significantly weakened in the high-risk group in both the GEO and TCGA cohorts.Conclusion: We successfully built a ferroptosis-related gene signature of significant predictive value for bladder cancer. These results suggest a novel research direction for targeted therapy of bladder cancer in the future.

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Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.776979 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chao Zhu ◽

Liqun Gu ◽

Mianfeng Yao ◽

Jiang Li ◽

Changyun Fang

Keyword(s):

Overall Survival ◽

Squamous Cell Carcinoma ◽

Regression Analysis ◽

Oral Squamous Cell Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Cox Regression Analysis ◽

Immune Related Gene

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

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Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-83120-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chunlei Wu ◽

Quanteng Hu ◽

Dehua Ma

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Related Gene ◽

Cox Regression Analysis ◽

Gene Pairs ◽

Immune Related Gene ◽

Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Bladder cancer

10.21203/rs.3.rs-333257/v1 ◽

2021 ◽

Author(s):

Liyuan Wu ◽

Feiya Yang ◽

Nianzeng Xing

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Gene Signature ◽

Survival Prediction ◽

Related Gene ◽

Cox Regression Analysis ◽

Prognostic Prediction

Abstract Background Bladder cancer (BC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Ferroptosis is related to a variety of biological pathways, including those involved in the metabolism of amino acids, lipids, and iron. However, the prognostic value of ferroptosis-related genes in BC remains to be further elucidated. Methods In this study, the mRNA expression profiles and corresponding clinical data of BC patients were downloaded from public databases. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature and validated it. Results Our results showed 12 differentially expressed genes (DEGs) were correlated with overall survival (OS) in the univariate Cox regression analysis (all adjusted P< 0.05). A 9-gene signature was constructed to stratify patients into two risk groups. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group (P < 0.001). The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups, especially in humoral immune response process. Conclusion In conclusion, a novel ferroptosis-related gene signature can be used for prognostic prediction in BC. Targeting ferroptosis may be a therapeutic alternative for BC.

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Scoring System Based on RNA Modification Writer-Related Genes to Predict Overall Survival and Therapeutic Response in Bladder Cancer

Frontiers in Immunology ◽

10.3389/fimmu.2021.724541 ◽

2021 ◽

Vol 12 ◽

Author(s):

Pu Zhang ◽

Zijian Liu ◽

Decai Wang ◽

Yunxue Li ◽

Yifei Xing ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Immune Cell ◽

Cox Regression ◽

Gene Signature ◽

Rna Modification ◽

Lasso Regression ◽

Related Gene ◽

Mutation Burden ◽

Selection Operator

IntroductionIt’s widely reported the “writer” enzymes mediated RNA adenosine modifications which is known as a crucial mechanism of epigenetic regulation in development of tumor and the immunologic response in many kinds of cancers. However, the potential roles of these writer genes in the progression of bladder cancer (BLCA) remain unclear.Materials and MethodsWe comprehensively described the alterations of 26 RNA modification writer genes in BLCA from the genetic and transcriptional fields and identified writer-related genes from four independent datasets. Utilizing least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression, we constructed a ten writer-related gene signature. After that, we confirmed the predictive and prognostic value of this signature on another six independent datasets and established a nomogram to forecast the overall survival (OS) and mortality odds of BLCA patients clinically.ResultsThe writer-related genes signature showed good performance in predicting the OS for BLCA patients. Moreover, the writer-related gene signature was related to EMT-related pathways and immune characteristics. Furthermore, the immune cell infiltration levels of CD8 T cells, cytotoxic cells, M1/2 macrophage cells and tumor mutation burden might be able to predict which patients will benefit from immunotherapy. This could also be reflected by the writer-related gene signature.ConclusionsThis signature might play an important role in precision individualized immunotherapy. The present work highlights the crucial clinical implications of RNA modifications and may help developing individualized therapeutic strategies for patients with BLCA.

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Identification of an 11-Autophagy-Related-Gene Signature as Promising Prognostic Biomarker for Bladder Cancer Patients

Biology ◽

10.3390/biology10050375 ◽

2021 ◽

Vol 10 (5) ◽

pp. 375

Author(s):

Chaoting Zhou ◽

Alex Heng Li ◽

Shan Liu ◽

Hong Sun

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Training Cohort ◽

Cox Regression Analysis ◽

Therapeutic Decisions

Background: Survival rates for highly invasive bladder cancer (BC) patients have been very low, with a 5-year survival rate of 6%. Accurate prediction of tumor progression and survival is important for diagnosis and therapeutic decisions for BC patients. Our study aims to develop an autophagy-related-gene (ARG) signature that helps to predict the survival of BC patients. Methods: RNA-seq data of 403 BC patients were retrieved from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database. Univariate Cox regression analysis was performed to identify overall survival (OS)-related ARGs. The Lasso Cox regression model was applied to establish an ARG signature in the TCGA training cohort (N = 203). The performance of the 11-gene ARG signature was further evaluated in a training cohort and an independent validation cohort (N = 200) using Kaplan-Meier OS curve analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox regression analysis. Results: Our study identified an 11-gene ARG signature that is significantly associated with OS, including APOL1, ATG4B, BAG1, CASP3, DRAM1, ITGA3, KLHL24, P4HB, PRKCD, ULK2, and WDR45. The ARGs-derived high-risk bladder cancer patients exhibited significantly poor OS in both training and validation cohorts. The prognostic model showed good predictive efficacy, with the area under the ROC curve (AUCs) for 1-year, 3-year, and 5-year overall survival of 0.702 (0.695), 0.744 (0.640), and 0.794 (0.658) in the training and validation cohorts, respectively. A prognostic nomogram, which included the ARGs-derived risk factor, age and stage for eventual clinical translation, was established. Conclusion: We identified a novel ARG signature for risk-stratification and robust prediction of overall survival for BC patients.

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Identification of an Immune-Related Gene Signature Based on Immunogenomic Landscape Analysis to Predict the Prognosis of Adult Acute Myeloid Leukemia Patients

Frontiers in Oncology ◽

10.3389/fonc.2020.574939 ◽

2020 ◽

Vol 10 ◽

Author(s):

Ruiqi Zhu ◽

Huishan Tao ◽

Wenyi Lin ◽

Liang Tang ◽

Yu Hu

Keyword(s):

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Myeloid Leukemia ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Hub Genes ◽

Cox Regression Analysis ◽

Immune Related Gene

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by highly heterogeneous molecular lesions and cytogenetic abnormalities. Immune disorders in AML and impaired immune cell function have been found to be associated with abnormal karyotypes in AML patients. Immunotherapy has become an alternative therapeutic method that can improve the outcomes of AML patients. For solid tumors, the expression patterns of genes associated with the immune microenvironment provide valuable prognostic information. However, the prognostic roles of immune genes in AML have not been studied as yet. In this study, we identified 136 immune-related genes associated with overall survival in AML patients through a univariate Cox regression analysis using data from TCGA-AML and GTEx datasets. Next, we selected 24 hub genes from among the 136 genes based on the PPI network analysis. The 24 immune-related hub genes further underwent multivariate Cox regression analysis and LASSO regression analysis. Finally, a 6 immune-related gene signature was constructed to predict the prognosis of AML patients. The function of the hub IRGs and the relationships between hub IRGs and transcriptional factors were investigated. We found that higher levels of expression of CSK, MMP7, PSMA7, PDCD1, IKBKG, and ISG15 were associated with an unfavorable prognosis of AML patients. Meanwhile, patients in the TCGA-AML datasets were divided into a high risk score group and a low risk score group, based on the median risk score value. Patients in the high risk group tended to show poorer prognosis [P = 0.00019, HR = 1.89 (1.26–2.83)]. The area under the curve (AUC) was 0.6643. Multivariate Cox Regression assay confirmed that the 6 IRG signature was an independent prognostic factor for AML. The prognostic role of the immune related-gene signature was further validated using an independent AML dataset, GSE37642. In addition, patients in the high risk score group in the TCGA dataset were found to be of an advanced age, IDH mutation, and M5 FAB category. These results suggested that the proposed immune related-gene signature may serve as a potential prognostic tool for AML patients.

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Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer

BMC Cancer ◽

10.1186/s12885-021-09006-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaotao Li ◽

Shi Fu ◽

Yinglong Huang ◽

Ting Luan ◽

Haifeng Wang ◽

...

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Survival Analysis ◽

Risk Score ◽

Survival Probability ◽

Cox Regression ◽

Risk Group ◽

Differentially Expressed ◽

Related Gene ◽

Cox Regression Analysis

Abstract Background Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients. Methods First, differentially expressed MRGs between BC and normal samples were identified and used to construct a protein-protein interaction (PPI) network and perform mutation analysis. Next, univariate Cox regression analysis was utilized to select prognostic genes, and multivariate Cox regression analysis was applied to establish an MRG signature for predicting the survival probability of BC patients. Moreover, Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive capability of the MRG signature. Finally, a nomogram based on the MRG signature was established to better predict the survival of BC. Results In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. Next, an MRG signature, including MAOB, FASN and LRP1, was established by using univariate and multivariate Cox regression analysis. Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group. Finally, Cox regression analysis showed that the risk score was an independent prognostic factor, and a nomogram integrating age, pathological tumor stage and risk score was established and presented good predictive ability. Conclusion We successfully constructed a novel MRG signature to predict the prognosis of BC patients, which might contribute to the clinical treatment of BC.

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Identification of a ferroptosis-related gene signature (FRGS) for predicting clinical outcome in lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11233 ◽

2021 ◽

Vol 9 ◽

pp. e11233

Author(s):

Sheng Wang ◽

Chunlei Wu ◽

Dehua Ma ◽

Quanteng Hu

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Gene Signature ◽

Tnm Stage ◽

Related Gene ◽

Cox Regression Analysis ◽

Prognostic Predictors

Background Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer. Ferroptosis, an oxidative, iron-dependent form of necrotic cell death, is highly associated with tumorigenesis and cancer progression. However, the prognostic value of ferroptosis progress in LUAD was still rarely be investigated. Methods Herein, we collected three mRNA expression profiles and 85 ferroptosis-related genes from public databases. The “limma” package was used to identify ferroptosis-related differentially expressed genes (DEGs). Univariate Cox regression analysis and LASSO regression analysis were applied to screen and develop a ferroptosis-related gene signature (FRGS) and a formula to calculate the risk score. Multivariate Cox regression analysis was implemented to determine independent prognostic predictors of overall survival (OS). The area under the receiver operating characteristic curve (AUC) and calibration plot were used to evaluate the predictive accuracy of the FRGS and nomogram. Results We developed a FRGS with five genes (CYBB, CISD1, FADD, SAT2, VDAC2). The AUC of the FRGS in TCGA cohort was 0.777 at 1-year, 0.721 at 3-year and 0.725 at 5-year, significantly superior to the AUC of TNM stage (1-year: 0.701, 3-year: 0.691, 5-year: 0.686). A similar phenomenon was observed in GEO cohort 1 and 2. Multivariate Cox regression analysis indicted TNM stage and risk score were independent prognostic predictors. Finally, we built a nomogram with TNM stage and FRGS, the AUCs of which markedly higher than that of FRGS or TNM stage alone. Conclusion We constructed a prognostic FRGS with five ferroptosis-related genes and a nomogram for predicting the 1-, 3- and 5-year survival rate of LUAD patients, which may provide a new understanding of the prognostic value of ferroptosis progress in LUAD and will benefit prognosis assessment of LUAD patients.

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A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽

10.1186/s12885-021-08360-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Guichuan Huang ◽

Jing Zhang ◽

Ling Gong ◽

Yi Huang ◽

Daishun Liu

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Cox Regression ◽

Lung Squamous Cell Carcinoma ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

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